[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83091-en":3,"doc-seo-83091-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83091,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","ExplAIner: A Declarative Query Language for Explaining Classification Models","ExplAIner proposes a declarative query-language framework for explainable AI, treating explanation notions as fixed queries evaluated over ML classification models. The work revisits FOIL and identifies key limitations: inability to express central optimality-based explanation queries and high evaluation complexity over decision trees. ExplAIner extends FOIL with layered structure and richer vocabulary, enabling abductive, contrastive, feature-based, and distance-based explanations. Complexity results bound evaluation in the Boolean hierarchy and show tractable computation for deterministic/decomposable circuits, with an Opt-FOIL optimization fragment yielding minimal explanations via SAT-based calls.","arXiv :2607 .06407v 1 [ cs .AI ] 7 Jul 2026  \nExplAIner: A Declarative Query Language for Explaining Classification Models  \nMARCELO ARENAS, Pontificia Universidad Católica de Chile, Chile PABLO BARCELÓ, Pontificia Universidad Católica de Chile, Chile DIEGO BUSTAMANTE, Pontificia Universidad Católica de Chile, Chile JOSE CARABALL, Pontificia Universidad Católica de Chile, Chile  \nMARÍA ALEJANDRA SCHILD, Pontificia Universidad Católica de Chile, Chile BERNARDO SUBERCASEAUX, Carnegie Mellon University, USA  \nThe XAI community has studied a wide range of queries and scores for explaining predictions of ML models. From a data management perspective, this proliferation of explanation notions calls for declarative query languages in which such notions can be specified, combined, and analyzed uniformly. In this paper, we develop such a framework for Boolean models. We first revisit FOIL, an interpretability query language for black-box models, and show that it has two fundamental limitations: it cannot express central optimality-based explanation queries, and its evaluation problem over decision trees is hard for every level of the polynomial hierarchy. We then introduce ExplAIner, a query language based on FOIL with an extended vocabulary and a layered structure. We show that ExplAIner can express a broad family of explanation notions, including abductive, contrastive, feature-based, and distance-based queries. We also prove that the evaluation problem for each query in ExplAIner belongs to the Boolean hierarchy over every class of Boolean models for which some basic predicates can be evaluated in polynomial time. In particular, that property holds for deterministic and decomposable Boolean circuits. Finally, we introduce Opt-FOIL, an optimization-oriented fragment of ExplAIner for computing explanations that are minimal with respect to strict partial orders, and prove that its evaluation problem is in FPNP under the same tractability assumptions. These complexity results have a direct algorithmic consequence: a fixed ExplAIner query can be evaluated with a fixed number of calls to a SAT solver, while a notion of explanation specified in Opt-FOIL can be computed with a polynomial number of such calls. This is particularly relevant in formal XAI, where SAT solvers have been successfully used to compute explanations for several classes of ML models.  \n1 Introduction  \nExplainability as a query-language problem. The increasing use of machine learning (ML) models in decision-making systems has created a pressing need for principled methods to understand the predictions produced by such models. This need has led to a large body of work in explainable AI (XAI) [8, 21, 22, 37], and in particular to a variety of queries, scores, and explanation notions aimed at identifying why a model classifies a given input in a particular way [15, 33, 34] . Examples include abductive explanations, contrastive explanations, counterfactual-style queries, and feature-necessity or feature-relevance notions [16, 23, 25, 40] .  \nFrom a data management perspective, this proliferation of explanation notions suggests a natural question: rather than designing a separate algorithm or formalism for each explanation task, can we develop a declarative language in which users specify what explanation they are looking for? This is in line with a long tradition in databases: complex computational tasks are exposed through query languages with well-defined syntax and semantics, while the study of their expressive power and evaluation complexity provides a principled understanding of what can be asked and  \nAuthors’ Contact Information: Marcelo Arenas, Pontificia Universidad Católica de Chile, Santiago, Chile; Pablo Barceló, Pontificia Universidad Católicade Chile, Santiago, Chile; Diego Bustamante, Pontificia Universidad Católica de Chile, Santiago, Chile; Jose Caraball, Pontificia Universidad Católicade Chile, Santiago, Chile; María Alejandra Schild, Pontificia Universid","cbCaipIbDlp0Hqmd","https://ap.wps.com/l/cbCaipIbDlp0Hqmd","pdf",1109939,2,1,44,"English","en",105,"# Introduction\n## Explainability as a query-language problem\n## Data complexity and tractability goals","[{\"question\":\"What problem does the paper address in explainable AI (XAI)?\",\"answer\":\"It addresses the need for principled ways to understand ML model predictions by unifying diverse explanation queries and notions under a single declarative language framework.\"},{\"question\":\"What are the limitations of FOIL identified in the paper?\",\"answer\":\"FOIL cannot express central optimality-based explanation queries, and evaluating it over decision trees is hard for every level of the polynomial hierarchy.\"},{\"question\":\"How does ExplAIner extend FOIL and what explanation types can it express?\",\"answer\":\"ExplAIner builds on FOIL with an extended vocabulary and layered structure, enabling queries for abductive, contrastive, feature-based, and distance-based explanation 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problem does the paper address in explainable AI (XAI)?","Question",{"text":75,"@type":76},"It addresses the need for principled ways to understand ML model predictions by unifying diverse explanation queries and notions under a single declarative language framework.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the limitations of FOIL identified in the paper?",{"text":80,"@type":76},"FOIL cannot express central optimality-based explanation queries, and evaluating it over decision trees is hard for every level of the polynomial hierarchy.",{"name":82,"@type":73,"acceptedAnswer":83},"How does ExplAIner extend FOIL and what explanation types can it express?",{"text":84,"@type":76},"ExplAIner builds on FOIL with an extended vocabulary and layered structure, enabling queries for abductive, contrastive, feature-based, and distance-based explanation 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